Supply and Demand Prediction Method of Public Bicycle Stations Based on Markov Chain

Abstract

The invention discloses a public bicycle station supply and demand prediction method based on a Markov chain. A balance stable equation relevant to station importance is established by constructing a transfer probability matrix of borrowed and returned vehicle by using the bicycle lease data of a public bicycle lease station terminal in order to predict the daily borrowing and returning demands of a station. The method has the positive effects that the classical method of Markov chain on probability statistics is applied in combination with the practical problem of a public bicycle lease station, and a practicable vehicle borrowing and returning supply and demand prediction method is provided, so that theoretical guidance is provided for the specific pile construction problem and balance scheduling problem of the public bicycle station. The method has a good industrial application prospect.

Description

technical field [0001] The invention belongs to the field of public bicycle system planning in traffic planning, in particular to a method for predicting the borrowing and returning demand of public bicycles. Background technique [0002] As an important part of the public transportation system, the public bicycle system solves the "last mile" terminal transportation demand that neither rail transit nor ground public transportation can meet. The system has the characteristics of convenient use, high accessibility, low fees, low-carbon environmental protection, etc. It can be used as a capillary in the public transportation system to provide "door-to-door" services, effectively meet the needs of short-distance travel, and greatly extend public transportation range of services. [0003] With the gradual establishment of public bicycle systems in various places, a series of problems have emerged in the popularization of use. Among them, there are two main problems. One is that...

AI Technical Summary

Problems solved by technology

The defect of this forecasting method is: the forecasting method is based on the characteristics of the city, and it is a forecasting rule obtained from a certain statistical law. It is not applicable to every city, especially in the face of large differences in city size and the behavior of customers choosing travel modes. Where the gap is large, the accuracy of the prediction results is greatly reduced

However, this kind of prediction also has the following problems: each traffic area may contain multiple public bicycle stations, and the importance of each station has obvious differences due to different geographical locations. The supply and demand relationship predicted by traditional forecasting methods has no The method guides the supply and demand forecasting of specific sites, which makes the supply and demand of specific sites not match the real demand. The phenomenon of citizens having no car to borrow and nowhere to park cannot be truly alleviated. Benefit from the people, be wasted

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